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This paper expands upon the classical Granger causality test with spatial dimensions to investigate the causal linkages between regional economic output and surface transportation infrastructure with a focus on the Northeast Corridor in the United States. To improve the validity of estimation, a panel data set of surface transportation infrastructure including highways, public intercity railways, and public transit, which are measured in real monetary value for the period 1991-2009, is adopted. Granger causality tests with integrations of various forms of spatial spillover interactions are analyzed in different spatial vector autoregressive models. The empirical results reveal that the spatial Granger causality test is able to capture a broader geographic scale of causality than the classical approach, but the causality between regional economic output and surface transportation infrastructure is sensitive to the model specifications. [ABSTRACT FROM AUTHOR]

The present study seeks to extend the depth and scope of an earlier study by Isserman (2004). In particular, using Isserman (2004) as a starting point and using more recent data [for the years 2005 through 2009], this study seeks to provide a broader and more in-depth perspective on the role and relative contribution of Regional Economics research to pertinent related scholarly literature as a whole. Interestingly, by taking into consideration the size of a subfield or field in terms of the number of its SSCI-journals, Regional Science journals may well manifest a higher impact than the journals of Economics and Geography. Hence, RSAI journals appear to be contributing quite productively, given the relative size of their field. [ABSTRACT FROM AUTHOR]

We employ a multi-method approach to more fully explore determinants of greater than expected rural county-level increases and decreases in the proportion of working poor in four states. An econometric model by Anderson, Goe, and Weng (2007) using 1990 and 2000 Census data in the North Central region of the U.S. supplies the error terms to identify our outlier counties. We show that counties performing better than expected may be more self-reliant than counties performing worse than expected (Michigan), that regional attributes contribute greatly to overall performance (Ohio), that the structure of local employment patterns also influences outcomes (Missouri), while devolution of federal government and long-term commuting patterns may also affect outcomes (South Dakota). Future efforts should attempt to replicate these research strategies and may serve to inform the direction of best practice in federal data collection efforts. [ABSTRACT FROM AUTHOR]